Matthew J Cracknell

Research Fellow in Earth Informatics, ARC TMVC Research Hub

Dr Matthew Cracknell is a Postdoctoral Research Fellow in Earth Informatics at the ARC Industrial Transformation Research Hub for Transforming the Mining Value Chain (TMVC). He also lectures 2nd and 3rd year geophysics students for the School of Physical Sciences (Earth Sciences) and conducts research at the ARC Centre of Excellence in Ore Deposits (CODES). His research focuses on the use of data mining and pattern recognition techniques for integrating and analysing geoscience data.

Biography

Matthew received a BSc (Hons) in geophysics from the University of Tasmania in 2009. He graduated from the University of Tasmania with a PhD in Computational Geophysics in 2014. Prior to his current position as a Postdoctoral Research Fellow, Matthew held numerous short-term research and teaching positions at the Centre of Excellence in Ore Deposits (CODES), Antarctic Climate & Ecosystems Cooperative Research Centre (ACE CRC) and the School of Humanities University of Tasmania. Matthew has been employed as a consultant geoscientist and GIS analyst, completing projects for Forestry Tasmania, Mineral Resources Tasmania (MRT), Blue Wren Consulting and the Environmental Protection Agency (EPA) oil spill response program via Ecomarine Consulting.

Research Themes

Matthew is a key member of the TMVC Research Hub and the Computational Geophysics and Earth Informatics group, led by Associate Professor Anya Reading, at The University of Tasmania. His research combines elements of both the Resources and Sustainability and Data, Knowledge and Decisions University research themes. Matthew's PhD thesis explored the use of data mining and machine learning techniques to identify and analyse patterns in high-dimensional geological, geophysical and geochemical data. Specific focus was placed on generating useful outputs such as robust measures of uncertainty and the development of methods for interpreting inferred relationships.

Currently, Matthew is researching novel approaches to adding value to digital scans of drillcore data, specifically related to the automated detection, extraction and classification of geological features. In addition, Matthew is applying the skills gained through his PhD to addressing the significant challenge of finding deeply buried ore deposits in the regolith dominated terrains of Australia. Matthew is also applying his unique skills to projects that aim to improve our understanding of large-scale tectonic architectures of the Australian and Antarctic continents.

Collaboration

Matthew is working closely with mining and resource partners through the TMVC Research Hub. He is also currently involved in collaborative research projects with the University of California, Riverside and the NASA Astrobiology Institute that aim to discriminate ore deposit styles and biological activity from pyrite trace element analyses. Matthew works closely with Geoscience Australia on a project that aims to identify subtle mineralisation signatures in continental-scale geochemical and geophysical data, and with the University of Canberra's Institute of Applied Ecology developing objective spatial data analysis methods for agricultural and soil conservation land management systems. He has completed research projects and still maintains close links with Mineral Resources Tasmania.

Awards

S.W. Carey Prize for the best BSc Honours thesis in the geological sciences at the University of Tasmania (2009)

Publications

Matthew has been the primary author of several peer-reviewed journal articles. These publications document the novel application of supervised and unsupervised learning algorithms to a diverse range of geoscience problems. He has also contributed to a number of conference abstracts presented at national and international geophysics conferences. Matthew regularly reviews journal articles for Computers & Geosciences and IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS).

Total publications

57

Highlighted publications

(1 outputs)

Year

Type

Citation

Altmetrics

2014

Journal Article

Cracknell MJ, Reading AM, 'Geological mapping using remote sensing data: a comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information', Computers and Geosciences, 63 pp. 22-33. ISSN 0098-3004 (2014) [Refereed Article]

Funding Summary

Projects

Mining operations are increasingly reliant on digital geological data to inform all aspects of ore deposit extraction, refinement and remediation. These data, e.g. drill core hyperspectral imagery, contain information on geological features and processes that are typically interpreted by geologists using drill core logs and geochemical assays. Despite this, converting high resolution hyperspectral imagery and associated mineralogical classifications into geological knowledge that informs mine-scale block models is yet to be fully realised. This PhD project will make use of unprecedented access to multi-scale digital geological data from a variety of porphyry and epithermal ore deposits with the overarching aim of defining and characterising geological features relevant to mining operations. Research activities are expected to include investigation, assessment and implementation of image processing and supervised and unsupervised machine learning methods for defining patterns in multi-scale geological data. The outcomes of this project are expected to facilitate the integration of large volumes of digital data from diverse sources with existing geological knowledge in order to develop the next generation of models for ore deposit exploitation. As this PhD project forms part of the ARC Transforming the Mining Value Chain (TMVC) AMIRA P1202 funded "Far-field and near mine footprints" project, specifically within Module 4 "The transition zone". This project will involve close collaboration with TMVC and AMIRA P2102 project industry and academic partners.

South-east Australia has a complex and fragmented geological history. Over the past 550 million years platetectonic processes have resulted in the formation of metal-rich mineral deposits. This project aims to develop andtest models for evaluating past tectonic processes and configurations, using both new and existing geological,geophysical and isotopic data. The information gained will be used to identify areas of high potential foreconomically valuable ore deposits, thereby enabling more efficient prioritisation of mineral exploration efforts.This will increase the probability of significant ore deposit discoveries leading to national economic benefit.

Research Supervision

Matthew provides support to several Higher Degree Research students – 'The Geofizz Nerds'. Matthew is looking to supervise Honours and PhD students in the fields of computational geophysics and geoinformatics.